Framework Integrations

Strathon instruments your agent framework's own extension points — callback handlers, plugins, event listeners — so connecting it takes two lines and no changes to your agent logic. All 10 integrations use first-class extension points where available. No monkey-patching.

bash

pip install "strathon[langgraph]"   # one framework
pip install "strathon[all]"         # all 10

Then in your code:

python

from strathon import Client, instrument

client = Client(api_key="stra_...", endpoint="http://localhost:4318")
instrument(client, frameworks=["langgraph"])

Supported frameworks

Framework	Integration	Guide
LangGraph	LangChain `BaseCallbackHandler`: intercepts tool calls before execution	Guide
CrewAI	Event listener on the CrewAI event bus	Guide
LangChain	Same callback handler as LangGraph	Guide
OpenAI Agents SDK	`TracingProcessor` extension point	Guide
Google ADK	First-class `BasePlugin`	Guide
Pydantic AI	First-class `AbstractCapability`	Guide
Claude Agent SDK	`query()` wrapper	Guide
AutoGen	`BaseChatAgent.on_messages` wrapper	Guide
OpenAI	Drop-in wrapper for `chat.completions.create`	Guide
Anthropic	Drop-in wrapper for `messages.create`	Guide

Which should I use?

If you are building production agents and not already committed to a framework, LangGraph is the most widely deployed choice for stateful, auditable workflows and is the best-supported integration here. If you already use a framework, pick its guide above; every integration captures the same trace data and enforces the same policies.

The raw model-SDK wrappers (OpenAI, Anthropic) instrument direct model calls rather than an agent framework. Use them when you call a model SDK directly without an orchestration layer.